A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy, PyTorch, JAX, or TensorFlow, allowing hybrid CPU-GPU-QPU computations. The same quantum circuit model can be run on different devices. Install plugins to run your computational circuits on more devices, including Strawberry Fields, Amazon Braket, Qiskit and IBM Q, Google Cirq, Rigetti Forest, and the Microsoft QDK.

Features

  • Makes PyTorch and TensorFlow quantum
  • Access all the devices
  • Follow the gradient
  • PennyLane is a proud member of the quantum open-source community
  • Sit back and learn about the field of quantum machine learning, explore key concepts, and view our selection of curated videos
  • Tutorials to introduce core QML concepts, including quantum nodes, optimization, and devices, via easy-to-follow examples

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License

Apache License V2.0

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Additional Project Details

Programming Language

Python

Related Categories

Python Software Development Software, Python Machine Learning Software, Python Neural Network Libraries

Registered

2022-08-15